|Using the Panchromatic band for water column correction |
to derive water depth and spectral bottom signature:
Landsat 8 OLIP bandset used for this work
Purple=1, Blue=2, Green=3, PAN=4, Red=5, NIR=6 and SWIR1=7
|Using pan sharpened images in this study |
Pan sharpening using Rstudio Brovey method
most variable atmosphere
Deglinting along Profile_Yellow
Glint regressions are excellent
Deglinting is excellent
Adjacency effect is fairly strong,
several kilometers wide
Calibration Blue vs Green
Calibration Coastal vs Green
Calibration Blue vs PAN
Calibration Blue, Green, Red, NIR
Calibration Coastal, PAN, Red, and NIR
CC BOA WCC
B average bottom brightness
In order to test 4SM against spectral variations of the bottom substrate,
I can enforce a depth over land to apply the water column attenuation from image calibration,
then process these "artificial shallow" pixels to see how well/bad this depth is retrieved.
|Result of this exercise |
From very shallow to very deep, the algorithm yields a surprisingly good estimation of depth;
there is hardly any increase in uncertainty as depth increases.
But, wait: this does not include the quantization noise (mmm...)!
I tried including 8_bits quantization of the computation of "artificial" pixels:
absolutely NO change either of av_Z4SM or std_Z4SM for Zland=20m.
|GSD=15 m, pan-sharpened. NO smoothing applied. |
Just the estimated depth is "noisy", as an expression of
|PAN solution |
I suppose that this good result is in part a marked benefit of using the PAN band.
|Add heterogeneous atmosphere/water |
This does not account for the effect of the natural variations of the water/atmosphere optical properties,
which can be very nasty in their own right, and increase dramatically deeper than ~half of the shallow depth range.
Profile Land A along the beach
Profile Land B through town
Profile Land_C through agriculture
|Depth applied to land pixels |
ZLand= 1 m
ZLand= 2 m
ZLand= 5 m
|Average depth retrieved for three profiles |
Zland=10 m==>9.56+-0.47 m